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Alert Policy and Rare-Event Metrics for Seven-Day-Ahead Agitation Forecasting in Passive Dementia Monitoring
Shakya S, Khadka R.. Research Square, 2026-07-14
Topic: Brain imaging, connectivity and assessment
Plain-language summary
Default thresholds yielded 20–23% precision with 94– 100% recall—unacceptable for caregiver-facing alerts. Optimized thresholds raised precision to 73–82% with 56.25% recall (BiL-STM: accuracy 91.15%, F1 0.667). At test scale (361 sequences, 8 patients), optimized BiLSTM generated roughly one alert per 6–7 flagged days versus one per 2–3 under default calibration, reducing expected false alarms by more than half. Rare-event agitation forecasting must be reported with explicit alert policies, not accuracy alone. Entropy-sequence models become clinically actionable only after operating-point governance analogous to clinical decision support deployment gates [14].
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Shakya, S., & Khadka, R. (2026). Alert Policy and Rare-Event Metrics for Seven-Day-Ahead Agitation Forecasting in Passive Dementia Monitoring. Research Square. https://doi.org/10.21203/rs.3.rs-10328423/v1
Vancouver
Shakya S, Khadka R. Alert Policy and Rare-Event Metrics for Seven-Day-Ahead Agitation Forecasting in Passive Dementia Monitoring. Research Square. 2026. doi:10.21203/rs.3.rs-10328423/v1.
BibTeX
@article{shakya2026-d32827ed,
author = {Shakya, S. and Khadka, R.},
title = {{Alert Policy and Rare-Event Metrics for Seven-Day-Ahead Agitation Forecasting in Passive Dementia Monitoring}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-10328423/v1},
url = {https://doi.org/10.21203/rs.3.rs-10328423/v1}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- Default thresholds yielded 20–23% precision with 94– 100% recall—unacceptable for caregiver-facing alerts.
- Optimized thresholds raised precision to 73–82% with 56.25% recall (BiL-STM: accuracy 91.15%, F1 0.667).
- At test scale (361 sequences, 8 patients), optimized BiLSTM generated roughly one alert per 6–7 flagged days versus one per 2–3 under default calibration, reducing expected false alarms by more than half.
- Rare-event agitation forecasting must be reported with explicit alert policies, not accuracy alone.
- Entropy-sequence models become clinically actionable only after operating-point governance analogous to clinical decision support deployment gates [14].
Study details
- Participants51
- Effect size56.25%; 91.15%
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 34 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design and randomized / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multicenter collaboration, so those were left out: it earned 21 of the 62 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 1 / 4 | 51 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 21 of the 62 points we could judge it on is 34 out of 100. |
| Total | 34 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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